CODEWITHSUNDEEP
conv-neural-networklstmlossbilstmspike
Zineb Adaika
Zineb Adaika

Reputation: 1

Loss and accuracy curves with spikes?

I'm currently working with CNN, LSTM, and BiLSTM as a hybrid algorithm, and these are the results I got for the accuracy and loss curves for the training and test sets. The issue is that I do not know why there is a spike at the beginning of both curves. Please tell me the reason and any solution.

enter image description here

I tried to change the layers by deleting and adding other layers, expecting that would change the result, but unfortunately nothing happened.

Upvotes: 0

Views: 32

Answers (1)

ProblemSolver
ProblemSolver

Reputation: 19

The spike at the beginning of your accuracy and loss curves is common and occurs because the network is adjusting its weights significantly during the early epochs. This happens when the model is "learning quickly" to minimize the loss and find the optimal weights. There are several reasons for this behavior. A high learning rate can cause rapid changes in weights, leading to unstable loss and accuracy in the initial stages. Random weight initialization also contributes to this, as the model starts from a random point and learns to find a good starting position during the first few updates. Additionally, using a small batch size can cause fluctuations due to the variance in gradients.

To address this issue, you can try a few strategies. Using a smaller learning rate or implementing a learning rate scheduler to gradually decrease it over the epochs can help. Applying advanced weight initialization techniques like Xavier or He initialization can provide a better starting point for training. Increasing the batch size can also stabilize gradient updates and reduce fluctuations. Finally, a warm-up strategy, where the learning rate starts small and gradually increases during the first few epochs, can smoothen the training process and reduce the initial spike.

Upvotes: 1

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